As many reading this report are undoubtedly aware, the use of predictive analytics in business continues to grow, and shows no signs of abating. The trends we highlighted in last year’s report – proliferation of Big Data initiatives, diversification of educational opportunities, and the blurring between data science and traditional predictive analytics – are only becoming more widespread. This year, we thought it would be beneficial to examine some of the key hiring market developments, as well as their implications for both predictive analytics job seekers and hiring authorities alike.

1. Quantitative Initiatives Continue to Grow

Over the past four years of collecting data on predictive analytics professionals for The Burtch Works Studies, the Financial Services and Advertising/Marketing Services industries have consistently employed over 50% of PAPs each year. However, as analytics use cases have continued to spread to other industries, the percentage of predictive analytics professionals employed in other industries has increased. This year, for the first time since we’ve started collecting this data, the number of predictive analytics professionals employed in Financial Services and Advertising/Marketing Services dropped below half – to 45% – while many other industries have seen an increase, such as Technology and Consulting.

Implications for predictive analytics professionals: Opportunities in numerous industries are booming, and emerging areas such as healthcare and insurance are seeing increasing amounts of investment in analytics initiatives.

Implications for employers: The proliferation of analytics initiatives has happened broadly across many industries. This diversification necessitates considering applicants from other industry verticals when hiring, in order to ensure access to the biggest talent pool. Because analytics skills are the key consideration and tend to be transferable across domains, it’s best to look outside a specific industry when searching for talent.

As we pointed out in both our 2016 Burtch Works Study for Predictive Analytics and our 2017 Burtch Works Study for Data Science, more and more predictive analytics professionals are incorporating typical data science tools and skills. The results of this year’s SAS, R, or Python survey of the statistical tool preferences of over 1,100 predictive analytics professionals and data scientists showed that open source tools R and Python have taken hold in the predictive analytics community, where SAS had been the mainstay only a few years before.

Professionals: Continuous learning has always been an important aspect of keeping your skills relevant in a quickly-evolving field like analytics, but now especially is the time to embrace learning new skills and tools. Having a wide range of tools you are comfortable with increases your marketability, but keep in mind that tools will continue to evolve, so continually evaluating your own skillset is paramount.

Employers: As new tools emerge, companies should invest in their teams by providing training and other opportunities to learn. Predictive analytics professionals see value in being able to use the latest tools and technologies, and many enjoy tackling new challenges. Candidates will often prioritize job opportunities that are committed to training and teaching them new skills.

Professionals: Being able to translate analysis into insight and action is key to making a difference. With predictive analytics, companies don’t just want to know “what?”, they also want to know “why?”, “what does this mean for our business?”, and “what does this mean we should do?”.

Employers: Look for professionals that can bridge the gap between having strong technical knowledge and being able to explain concepts in layman’s terms. The better your team can collaborate with other business teams, the more value they’ll be able to provide.

4. Analytics Needs Ambassadors

Continuing our advice from this year’s Burtch Works Study for Data Science, finding leaders that are able to function as ambassadors and evangelists for the analytics team is important, especially when growing a new team. For a new capability to succeed, the leader and the team they hire must be able to adequately convey the value their team provides, as well as develop partnerships and collaborate with other leaders in the business.

Professionals: Simply providing or executing analysis is not sufficient when establishing the analytics team’s value within an organization. Data evangelism is a key attribute for analytics leaders, but analytics professionals at all levels should be prepared to serve as advocates for the use of analytics, by, for example, providing insight into specific ways the team can contribute additional value. Data evangelism at all career levels will help develop a strong data culture throughout the company.

Employers: When hiring senior leaders, look for those who can be an influencer as well as a strategic leader. Regardless of whether it is a new team or an established one, it is important to have a leader in place that can forge partnerships with other members of senior leadership. This skillset can ensure that the analytics team has the proper tools and support, and so is able to provide value enterprise-wide, as well as build confidence in the analytics team.

It continues to be an exciting time in analytics, not only for professionals looking to get into the field, but also for established professionals, companies looking to hone their business approaches, and startups springing up to address the growing needs of data-driven companies everywhere. With each year that we examine hiring market trends in predictive analytics, one trend has remained constant: opportunities are continuing to multiply.

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